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Understanding students' evaluations of professors using non-negative matrix factorization.

Necla Gündüz1, Ernest Fokoué2

  • 1Faculty of Science, Department of Statistics, Gazi Üniversity, Ankara, Turkey.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

Student evaluations of university professors reveal strong links between attendance and scores. Machine learning techniques uncover patterns in student feedback, highlighting dedication

Keywords:
Likert scale questinaireNonnegative matrix factorizationPattern recognitionRandom forestStudent evaluations of the professorsZero variation

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Area of Science:

  • Educational Data Mining
  • Machine Learning Applications
  • Higher Education Research

Background:

  • University professor evaluations are crucial for academic quality.
  • Understanding student feedback patterns is essential for improving teaching.
  • Existing methods may not fully capture the nuances of student evaluations.

Purpose of the Study:

  • To apply advanced machine learning techniques to analyze student evaluations of university professors.
  • To identify key patterns and relationships within student feedback data.
  • To explore the connection between student dedication and their evaluation scores.

Main Methods:

  • Utilized Nonnegative Matrix Factorization (NMF) for data analysis.
  • Employed state-of-the-art statistical machine learning techniques.
  • Applied Kullback-Leibler divergence as the loss function, suitable for the data type.

Main Results:

  • Identified significant patterns in student evaluations from Gazi University.
  • Revealed a strong association between student attendance (dedication) and assigned scores.
  • Demonstrated the effectiveness of NMF and machine learning in extracting meaningful insights.

Conclusions:

  • Student seriousness and dedication, indicated by attendance, correlate with professor evaluations.
  • Machine learning offers powerful tools for in-depth analysis of educational data.
  • Further research into student evaluation aspects is recommended for comprehensive understanding.